| Convolutional neural networks analyzed via convolutional sparse coding V Papyan, Y Romano, M Elad The Journal of Machine Learning Research 18 (1), 2887-2938, 2017 | 190 | 2017 |
| Multi-scale patch-based image restoration V Papyan, M Elad IEEE Transactions on image processing 25 (1), 249-261, 2015 | 165 | 2015 |
| Convolutional dictionary learning via local processing V Papyan, Y Romano, J Sulam, M Elad Proceedings of the IEEE International Conference on Computer Vision, 5296-5304, 2017 | 99 | 2017 |
| Working locally thinking globally: Theoretical guarantees for convolutional sparse coding V Papyan, J Sulam, M Elad IEEE Transactions on Signal Processing 65 (21), 5687-5701, 2017 | 94* | 2017 |
| Multilayer convolutional sparse modeling: Pursuit and dictionary learning J Sulam, V Papyan, Y Romano, M Elad IEEE Transactions on Signal Processing 66 (15), 4090-4104, 2018 | 91 | 2018 |
| Neural proximal gradient descent for compressive imaging M Mardani, Q Sun, S Vasawanala, V Papyan, H Monajemi, J Pauly, ... arXiv preprint arXiv:1806.03963, 2018 | 70 | 2018 |
| Theoretical foundations of deep learning via sparse representations: A multilayer sparse model and its connection to convolutional neural networks V Papyan, Y Romano, J Sulam, M Elad IEEE Signal Processing Magazine 35 (4), 72-89, 2018 | 67 | 2018 |
| Recurrent generative adversarial networks for proximal learning and automated compressive image recovery M Mardani, H Monajemi, V Papyan, S Vasanawala, D Donoho, J Pauly arXiv preprint arXiv:1711.10046, 2017 | 51 | 2017 |
| The full spectrum of deepnet hessians at scale: Dynamics with sgd training and sample size V Papyan arXiv preprint arXiv:1811.07062, 2018 | 31 | 2018 |
| Measurements of three-level hierarchical structure in the outliers in the spectrum of deepnet hessians V Papyan arXiv preprint arXiv:1901.08244, 2019 | 19 | 2019 |
| Prevalence of neural collapse during the terminal phase of deep learning training V Papyan, XY Han, DL Donoho Proceedings of the National Academy of Sciences 117 (40), 24652-24663, 2020 | 17 | 2020 |
| Degrees of freedom analysis of unrolled neural networks M Mardani, Q Sun, V Papyan, S Vasanawala, J Pauly, D Donoho arXiv preprint arXiv:1906.03742, 2019 | 5 | 2019 |
| Traces of Class/Cross-Class Structure Pervade Deep Learning Spectra V Papyan Journal of Machine Learning Research 21 (252), 1-64, 2020 | 3 | 2020 |
| Multimodal latent variable analysis V Papyan, R Talmon Signal Processing 142, 178-187, 2018 | 2 | 2018 |
| Prediction of Neurosurgical Hemorrhage Control and Instrument Detection Using Deep Learning GG Kugener, DJ Pangal, T Cardinal, C Collet, E Lechtholz-Zey, ... | 1 | 2021 |
| Global Versus Local Modeling of Signals V Papyan, M Elad Computer Science Department, Technion, 2017 | | 2017 |
| Convolutional Neural Networks Analyzed via Convolutional Sparse Coding L Thresholding, MLCSC Model, V Papyan, Y Romano, M Elad | | |
| Theoretical Foundations of Deep Learning via Sparse Representations V Papyan, Y Romano, J Sulam, M Elad | | |
| Working Locally Thinking Globally: Guarantees for Convolutional Sparse Coding V Papyan, J Sulam, M Elad | | |
| Analyzing Convolutional Neural Networks Through the Eyes of Sparsity V Papyan, Y Romano, M Elad | | |
Michael EladProfessor of Computer Science, Technion, IsraelVerified email at cs.technion.ac.il
